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1. P. Anil Kumar et al Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1600-1603
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Loss Reduction in Radial Distribution Network by Optimal
Placement of Type – I DG Using Modified Direct Search
Algorithm
P. Anil Kumar1
, B. Shankar Prasad2
PG Student, Dept. of EEE, AVANTHI’S St. Theressa Institute of Engineering & Technology, Garividi,
Andhra Pradesh, India 1
Associate Professor, HOD, Dept. of EEE, AVANTHIS’S St. Theressa Institute of Engineering & Technology,
Garividi, Andhra Pradesh, India 2
ABSTRACT
In this paper, Direct Search Algorithm is implemented to determine the optimal sizes of Type – I Distributed
Generators (DGs) and optimal locations such that maximum possible reduction in real power loss is obtained.
Type – I DG injects both active and reactive powers. The algorithm searches for all possible locations in the
system for a particular size of DG and places it at the bus which gives maximum reduction in active power loss.
The optimal sizes of DGs are chosen to be standard sizes i.e., discrete sizes of DGs are considered. The
algorithm is tested on 33 bus and 69 bus Radial Distribution Systems. The loss reduction obtained in this paper
for the 33 bus and 69 bus test systems are highest compared to other techniques in the literature. Power factor of
the DG is considered in this study is 0.85.
On 33 bus system, without placement of DGs the loss is 211 kW whereas after placement it is 15.50 kW. There
is a reduction of 92.65% in the losses. Before placement of DGs, the power loss is 5.37% of the total power
supplied by the slack bus. After optimal placement by the MODIFIED DSA algorithm, the active power loss is
0.415% of the total power supplied by the system. On 69 bus system, without placement of DGs the loss is 225
kW whereas after placement is 5.75 kW. There is a reduction of 97.44% in the losses. Before placement of DGs,
the power loss is 5.58% of the total power supplied by the slack bus. After optimal placement by the
MODIFIED DSA algorithm, the active power loss is 0.15% of the total power supplied by the system.
As the loss is less than 2%, the system is considered as Loss Less Distribution System. It is implemented using
MATLAB/Simulink.
Keywords: Loss Less Distribution, Optimal DG Placement, Distribution Systems, Modified Direct Search
Algorithm (MODIFIED DSA).
I. INTRODUCTION
Optimal capacitor placement is implemented
for improving the voltage profile and reducing the
power loss. Optimal DG placement is implemented
for reduction of active power loss and to improve the
reliability of the system. Very few papers have
addressed the concept of minimizing the active loss
by placing both DGs and Capacitors at their optimal
locations. This concept works well for the
developing countries like India, where the 11KV rural
distribution feeders are too long. The voltages at the
far end of many such feeders are very low with very
poor voltage regulation.
The computational methods used in the
analysis and design of distribution systems are not as
robust as they are in transmission systems. In
particular, the design of compensation systems for
radial distribution system has become very complex
because, the system does not fit into the usual
optimization methods used in transmission system. In
this paper, only one phase of Direct Search
Algorithm is used according to the requirement of
Type-1 DG. Capacitive compensation is not required
in this case as reactive power is injected by DG itself.
In the case of Type-2 and Type-3 DG, capacitive
compensation is separately required. This algorithm is
an extension of Direct Search Algorithm for
Capacitive compensation proposed by M. Ramalinga
Raju et. al.[1].
The technical merits of DG implementation
include voltage support, energy-loss reduction,
release of system capacity, and improve utility system
reliability [2]. By supplying power during peak load
periods DG can best serve as a price hedging
mechanism. Numerous techniques are proposed so far
to address the viability of DGs in power system.
Besides, several optimization tools, including
artificial intelligence techniques, such as genetic
algorithm (GA), Tabu search, etc., are also proposed
for achieving the optimal placement of DG. An
optimization approach using GA for minimizing the
cost of network investment and losses for a defined
planning horizon is presented in [3]. The method for
optimal placement of DG for minimizing real power
RESEARCH ARTICLE OPEN ACCESS

2. P. Anil Kumar et al Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1600-1603
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losses in power distribution system using GA is
proposed in [4].
The gradient and second order methods to
determine the optimal location for the minimization
of losses is employed in [5]. An iterative method that
provides an approximation for the optimal placement
of DG for loss minimization is demonstrated in [6].
Analytical methods for determining optimal location
of DG with the aim of minimizing power loss are
proposed in [7]. Optimal placement of DG with
Langrangian based approach using traditional pool
based Optimal Power Flow and voltage stability
constrained Optimal Power Flow formulations is
proposed in [8].
Carpinelli et al. implemented [9] non-linear
programming technique for capacitor placement on
three phase unbalanced system. Wang et al.
implemented [10] integer programming technique,
and Tabu search was used by Huang et al. [11] for
optimal capacitor placement. Grainger implemented
equal area criterion [12] and genetic algorithm
applied to capacitor placement by Dlfanti [13] for
determining optimal sizes of capacitors. Das applied
Fuzzy- GA method for capacitor placement problem
[14]. Sydulu and Reddy applied Index Vector to
capacitor placement problem [15], Prakash and
Sydulu applied particle swarm optimization for
optimal capacitor placement problem [16]. Safigianni
and Salis presented optimum VAr control of radial
primary power distribution networks by shunt
capacitor installation [17]. Das implemented genetic
algorithm [18], Hsiao implemented Fuzzy-genetic
algorithm for [19] for optimal capacitor placement
problem. Huang applied immune multi objective
algorithm for capacitor placement problem [20].
Kannana et al. applied Fuzzy-Differential Algorithm
[21], Srinivasa Rao et al. applied plant growth
algorithm for optimal capacitor placement problem
[22].
DGs are considered as small power
generators that complement central power stations by
providing incremental capacity to power system. DGs
may never replace the central power stations.
However, penetration and viability of DG at a
particular location is influenced by technical as well
as economic factors.
The MODIFIED DSA algorithm
implemented, with a possible expert interaction yields
optimal locations with suitable sizes of DGs, results
in minimum active power loss. The algorithm is
implemented on 33 Bus and 69 Bus Standard Test
Systems. 69 Bus data is available in [23]. Type -1 DG
injects active power and reactive power into the
system as mentioned in [24].
II. THE MODIFIED DSA
ALGORITHM
The DSA algorithm is slightly modified for
Type-1 DG placement in radial distribution system
with source bus as slack bus and all other load buses
as PQ buses. The algorithm implemented is
described in following steps for deciding the optimal
sizes of DGs and their locations (only load buses).
The algorithm is presented in the following steps:
1. Calculate the total active power load in the
system. To determine the optimal sizes of DGs, a
number of options having group of various DG sizes
are to be tried. A tolerance index is chosen. Losses
under any option should be less than the tolerance
index for convergence. All possible options may be
enlisted.
2. Let a(t) be the number of DGs tth
option, 't' ranging
from 1 to d where ‘d’ is the total number of options.
a(1) , the first option is with single DG, the P (active
power) of which is nearest to the total KW load,
placed at all load buses, in turn, and load flow study is
conducted. The line losses are determined. If the
lowest loss satisfies the tolerance criterion, the
process can be terminated. The size and location of
DG are considered as the optimal solution.
3. In one set of DGs a(t), the first DG is kept at all
load buses in turn, and the location for which losses
are the lowest is considered as the optimal location
for that DG. Placing this DG at that load bus, the
procedure is repeated for placing the second DG at all
load buses in turn and deciding the optimal location
for the second DG. This procedure is repeated for all
DGs.
4. The options a(2) to a(d) are sequenced taking more
and more number of DGs of smaller size such that the
total DG capacity is nearest to the total KW of the
system. System losses are found out for each
combination and checked for tolerance. If the
tolerance is acceptable, process can be terminated.
III. RESULTS
The standard sizes of DGs, and their
corresponding reactive power injections are presented
in Table I at 0.85 power factor.
Table I : Different DGs considered for
placement
S. No.
kW
capacity
kVAr
capacity
1 1000 619.74
2 500 309.87
3 300 185.92
4 200 123.94
5 100 61.97
The MODIFIED DSA Algorithm is
implemented on 69 - Bus System. The total active
and reactive power demand of the system is 3802.19
kW and 2694.60 kVAr respectively. Without
placement of DGs the loss is 225 kW. After placing

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ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1600-1603
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Type-1 DGs the loss is 5.75 kW. There is a reduction
of 97.44% in the losses. Before placement of DGs ,
the power loss is 5.58% of the total power supplied by
the slack bus. After optimal placement by the
MODIFIED DSA algorithm, the active power loss is
0.15% of the total power supplied by the system.
The MODIFIED DSA Algorithm is
implemented on 33 - Bus System. The total active
and reactive power demand of the system is 3715 kW
and 2300 kVAr respectively. Without compensation,
the losses are 211kW. After placing Type-1 DGs
the loss is 15.50 kW. Before placement of DGs, the
loss is 5.37% of the total power supplied by Slack
Bus. After placement, the loss is 0.415%of the total
power supplied by the slack bus.
The Table II shows the best combination of
DGs with location for 69 bus system and fourth
column shows active power loss after placing
capacitors in turn. Fig. 1 shows the reduction in
active power loss by optimal placement of DGs. X-
axis shows the number of DG mentioned in the order
of Table II.
The Table III shows the best combination of
DGs with location for 33 bus system and fourth
column shows active power loss after placing
capacitors in turn. Fig. 2 shows the reduction in
active power loss by optimal placement of DGs. X-
axis shows the number of DG mentioned in the order
of Table III.
Table- II: Type -1 DG placement on 69- bus system
using Direct Search Algorithm
S.
No
Active
Power
supplied
by the
DG (kW)
Reactive
Power
supplied
by DG
(kVAr)
Min Loss
Location
Active
power
loss after
placing
the DGs
in turn
( kW
1 1000 619.74 61 64.80
2 1000 619.74 61 24.29
3 500 309.87 18 9.57
4 300 185.92 50 8.11
5 300 185.92 11 6.77
6 300 185.92 49 6.03
7 200 123.94 49 5.86
8 100 61.97 46 5.75
Fig. 1 Reduction in active power loss by Type-1 DG
placement
Table- III: Type -1 DG placement on 33- bus system
using Direct Search Algorithm
S.
No
Active
Power
supplied
by the
DG (kW)
Reactive
Power
supplied
by DG
(kVAr)
Min Loss
Location
Active
power
loss after
placing
the DGs
in turn
( kW
1 1000 619.74 30 93.79
2 1000 619.74 11 35.47
3 500 309.87 25 21.58
4 500 309.87 24 17.7
5 100 61.97 22 16.87
6 100 61.97 26 16.18
7 100 61.97 21 15.78
8 100 61.97 5 15.50
Fig. 2 Reduction in active power loss by Type-1 DG
placement
IV. CONCLUSIONS
In this paper, Direct Search Algorithm is
implemented to determine the optimal sizes of
Distributed Generators (DGs) with their optimal
locations in 33 Bus and 69 Bus Radial Distribution
System so that maximum possible reduction in real
power loss is obtained. The optimal sizes of Type -1
DGs are chosen to be standard sizes i.e., discrete sizes
of DGs are considered.
On 33 bus system, without placement of
DGs the loss is 211 kW whereas after placement it is
15.50 kW. There is a reduction of 92.65 in the losses.
Before placement of DGs , the power loss is 5.37% of
the total power supplied by the slack bus. After
optimal placement by the MODIFIED DSA
algorithm, the active power loss is 0.415% of the total
power supplied by the system. On 69 bus system,
without placement of DGs the loss is 225 kW whereas
after placement it is 5.75 kW. There is a reduction of
97.44% in the losses. Before placement of DGs , the
power loss is 5.58% of the total power supplied by the
slack bus. After optimal placement by the
MODIFIED DSA algorithm, the active power loss is
0.15% of the total power supplied by the system.
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4. P. Anil Kumar et al Int. Journal of Engineering Research and Application www.ijera.com
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